---
title: "Indicadores"
author: "Universidad del Rosario"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library("flexdashboard")
library("plotly")
library("dplyr")
library("lattice")
library("sp")
library("rgdal")
library("rgeos")
library("maptools")
library("plyr")
library("gapminder")
library("scales")
library("reshape2")
library("tidyverse")
# library("Rcpp")
# Lectura de los datos
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
```
Indicadores I
=======================================================================
Row
-----------------------------------------------------------------------
### Número de contratos por municipio
```{r}
datos <- read.delim("../Secop2limpioAA.txt")
shape <- readShapeSpatial("../Mapas/COL_adm2.shp", repair = T)
deptos <- table(datos$Departamento) %>% data.frame() %>% .[-1,]
names(deptos) <- c("Departamento", "Contratos")
deptos$Departamento <- toupper(deptos$Departamento) %>% iconv(., to="ASCII//TRANSLIT")
## Conversiones necesarias sobre shape
np_dist <- fortify(shape, region = "NAME_2")
np_dist$id <- iconv(np_dist$id, "UTF-8", "latin1") %>% toupper(.) %>% iconv(., to="ASCII//TRANSLIT")
k <- which(np_dist$id== "SANTAFE DE BOGOTA") # Caso aislado - Hablar con CCE
np_dist$id[k] <- "BOGOTA"
p <- ggplot() + geom_map(data = np_dist, aes(map_id = id), map = np_dist, color = "aliceblue", fill = "gray")+
geom_map(data = deptos, aes(map_id = Departamento, fill = Contratos),
map = np_dist) + expand_limits(x = np_dist$long, y = np_dist$lat)+ylim(c(-5,13)) + xlim(c(-84,-64))
p
```
### Porcentaje de contratos por segmento
```{r}
segmento <- table(datos$Segmento) %>% data.frame()
names(segmento) <- c("Segmento", "Cantidad")
colors <- c("#1aa3a3", "#b2e5e5","#0b5e56", "#86becb" )
p <- plot_ly(segmento, labels = ~Segmento, values = ~Cantidad, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste(Cantidad, 'Contratos'),
marker = list(colors = colors,
line = list(color = '#FFFFFF', width = 1)),
#The 'pull' attribute can also be used to create space between the sectors
showlegend = FALSE) %>%
layout(title = '',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
p
```
Row
-----------------------------------------------------------------------
### Medias de los valores de los contratos por municipio
```{r}
valorContratos <- ddply(datos, .(Departamento), summarize,
Medias = mean(as.numeric(as.character(Valor.Contrato)), na.rm = T)) %>% .[-1,]
valorContratos$Departamento <- toupper(valorContratos$Departamento) %>% iconv(., to="ASCII//TRANSLIT")
ggplot() + geom_map(data = np_dist, aes(map_id = id), map = np_dist, color = "aliceblue", fill = "gray")+
geom_map(data = valorContratos, aes(map_id = Departamento, fill = Medias),
map = np_dist) + expand_limits(x = np_dist$long, y = np_dist$lat) +
scale_fill_gradient2(low = muted("red"), mid = "deepskyblue", midpoint = mean(valorContratos$Medias), high = muted("darkblue"), limits = c(0, max(valorContratos$Medias)))+
ylim(c(-5,13)) + xlim(c(-84,-64))
```
### Tipos de COntrato y segmentos de contrato
```{r}
colors <- rainbow(10)
df <- table( datos$Modalidad.Contratacion, datos$Segmento) %>% data.frame()
names(df) <- c("Tipo_Contrato", "Segmento", "Contratos")
p <- plot_ly(data = df, x =~ Segmento, y=~Contratos, color= ~Tipo_Contrato, text = ~paste(Tipo_Contrato, Contratos, 'Contratos', color = colors))
p
```
Indicadores II
=======================================================================
Row
-----------------------------------------------------------------------
### stat_density Example
```{r}
dfGamma = data.frame(nu75 = rgamma(100, 0.75),
nu1 = rgamma(100, 1),
nu2 = rgamma(100, 2))
dfGamma = stack(dfGamma)
p <- ggplot(dfGamma, aes(x = values)) +
stat_density(aes(group = ind, color = ind),position="identity",geom="line")
ggplotly(p)
```
### Add Conditional Density Curves to Plot
```{r}
# dim1 <- c(rnorm(100, mean=1), rnorm(100, mean=4))
# dim2 <- rnorm(200, mean=1)
# cat <- factor(c(rep("a", 100), rep("b", 100)))
# mydf <- data.frame(cbind(dim2, dim1, cat))
# p <- ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
# geom_point() +
# stat_density(aes(x=dim1, y=(-2+(..scaled..))),
# position="identity", geom="line")
# stuff <- ggplot_build(p)
# xrange <- stuff[[2]]$ranges[[1]]$x.range # extract the x range to make the
# # new densities align with y-axis
## Get densities of dim2
# ds <- do.call(rbind, lapply(unique(mydf$cat), function(lev) {
# dens <- with(mydf, density(dim2[cat==lev]))
# data.frame(x=dens$y+xrange[1], y=dens$x, cat=lev)
# }))
#
# p <- p + geom_path(data=ds, aes(x=x, y=y, color=factor(cat)))
#
# ggplotly(p)
```
Row
-----------------------------------------------------------------------
### geom_density and facet_wrap Together
```{r}
dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value", "State_CD")
dd <- data.frame(
predicted = rnorm(72, mean = 2, sd = 2),
state = rep(c("A", "B", "C"), each = 24)
)
grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
data.frame(
predicted = grid,
density = dnorm(grid, mean(df$predicted), sd(df$predicted))
)
})
p <- ggplot(dd, aes(predicted)) +
geom_density() +
geom_line(aes(y = density), data = normaldens, colour = "red") +
facet_wrap(~ state)
ggplotly(p)
```
### Density and Scatterplot Overlay Using geom_density
```{r}
df <- data.frame(x <- rchisq(1000, 10, 10),
y <- rnorm(1000))
p <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.5) +
geom_density_2d() +
theme(panel.background = element_rect(fill = '#ffffff'))
ggplotly(p)
```
